It is known from the past that it is necessary to analyze the usage situation and/or operational state of a network and to monitor for abnormalities based on such analysis result.
In the past, a variety of algorithms for analyzing the usage situation and/or operational state of an IP network have been developed. More specifically, there are the technologies disclosed in Patent Literature 1 to 4 listed below, for example.
The algorithm disclosed in Patent Literature 1 calculates the appearance frequency of respective values in a data series (i.e., totals the number of errors detected for each IP address) for IP packets flowing on an IP network, and also totals the numbers of errors detected for each IP address and obtains IP addresses or routes that are the cause of a certain proportion of errors or higher.
The algorithm disclosed in Patent Literature 2 calculates statistics by carrying out statistical analysis on IP packets flowing on an IP network and detects abnormalities from a degree of deviation of such statistics in comparison with historical statistics.
In addition, the algorithm disclosed in Patent Literature 3 detects deterioration in the quality of a network via determinations made using an RF method (Random Forest method) on IP packets flowing on an IP network. The technology disclosed in Patent Literature 3 carries out learning in advance for a plurality of parameters by inputting normal state values as correct values (a teacher input) for determinations and detects quality deterioration from the parameters relating to the present IP network based on the results of such learning.
In addition, the algorithm disclosed in Patent Literature 4 carries out multi-resolution analysis on time series information (for example, transitions in the amount of traffic on an IP network), also carries out learning by inputting such resolution components into a learning apparatus that uses a neural network (neuro), and predicts time series information based on the results of such learning.